Computer vision systems are based on information obtained from various measuring devices. Information can be measured using e.g. a laser device, a measuring head or via recognition from an image. The information obtained can be utilized e.g. in quality control systems, where, on the basis of this information, it is possible to determine e.g. the correctness of shape of an object, coloring errors or the number of knots in sawn timber.
A computer vision system is generally formed from cameras. Traditional computer vision systems comprised only one camera, which took a picture of the object. By processing the picture, various conclusions could be drawn from it. By using different algorithms, it is possible to distinguish different levels in images on the basis of their border lines. The border lines are identified on the basis of intensity conversion. Another method of recognizing shapes in an image is to connect it to masks and filters so that only certain types of points will be distinguished from the image. The patterns formed by the points in the image can be compared to models in a database and thus recognized.
In a three-dimensional computer vision system, several cameras are used. To determine a three-dimensional coordinate, an image of the same point is needed from at least two cameras. A truly three-dimensional computer vision system therefore comprises several cameras. The points are usually formed on the surface of the object via illumination. The illumination is typically implemented using a laser. The point is imaged by cameras calibrated in the same coordinate system with the illuminating device, a laser pointer. The same points imaged by different cameras are called corresponding points. When an image of the point can be produced by at least two cameras and corresponding points can be identified, then it is possible to determine three-dimensional coordinates for the point. For the same position, a number of points are measured. The set of points thus formed is called a point cloud.
In earlier methods, point clouds have been formed by illuminating one point at a time, in which case there are no problems regarding recognition of the corresponding point. When only one point on the object surface is visible to the cameras, they are all able to associate the measured data with the same point. Measuring one point at a time means that the pointer has to be moved every time between points, which is why this measuring method is slow.